The term self-organizing networks (SON) identifies the next generation technology for planning, optimization, and healing of wireless cellular networks. Although this technology is under discussion mainly for 3GPP LTE, the ideas behind SON will also be adapted for legacy cellular network technologies.
SOCRATES (e.g., in SOCRATES web page. Online: http://www.fp7-socrates.org, Feb. 26, 2012) was a project funded by the European Union between 2008 and 2010 with the aim of developing SON methods and algorithms for LTE mobile networks. The concepts given by the SOCRATES project provide a holistic framework to design SON algorithms and to reveal control parameter interdependencies and interactions among different algorithms. Multiple processes can be aggregated to so-called use cases, which may be independent or may interact since they can operate on common control parameters. Examples of SON use cases for network optimization are mobility load balancing (MLB), coverage and capacity optimization (CCO), and mobility robustness optimization (MRO). Each of these is expected to run independently in a certain deployment area of the cellular network and to address issues related to imbalanced load between cells, coverage holes or low signal-to-interference-and-noise ratios (SINRs), or handover failures by changing parameters defined in the configuration management (CM) of the cellular network. These autonomously running SON use case implementations naturally run into problems of conflicting parameter changes. For that reason, a SON coordinator is necessary for resolving possible parameter conflicts. The coordination is considered as the most critical challenge to meet and, therefore, coordination mechanisms have to be developed carefully. In SOCRATES, so-called heading or tailing coordination of conflicting parameters (before or after the independently determined parameters changes) is favored.
Drawbacks of this state of the art include:                need for complex policies to coordinate the parameterization of conflicting single use case implementations        heading or tailing, hence need for additional coordination of parameters of otherwise independently running SON optimization use case implementations        
A theoretical approach to the unified treatment of user association and handover optimization based on cell loads is presented in H. Kim et al., “Distributed α-Optimal User Association and Cell Load Balancing in Wireless Networks”, IEEE/ACM Transactions on Networking 20:1, pp. 177-190 (2012). Drawbacks of this work include:                not possible to predict the effect of a sudden change in the network configuration        is not compatible with the 3GPP standards                    Provides partitioning of cells, but no base station individual power offset for the received power of the base station's pilot or reference signal to be used to increase the base stations serving area for the purpose of user association for admission control, cell reselection in silent mode, and handover in active mode            Assumes that the UEs can take a decision on cell selection based on knowledge of the loads of surrounding base stations; however, in 3GPP the UEs only measure power levels and report them to the BS, where all decisions are taken                        is not able to estimate and predict base station loads and load changes in the future since BSs measure their average utilizations, but do not calculate the average loads        does not explicitly include the BS load in the SINR estimations, BS are not aware of the load of neighboring cells        a user location is not guaranteed to be served        does not include a load constraint for a cell/base station        
Another theoretical framework in the field of the invention is presented in Iana Siomina and Di Yuan, “Analysis of Cell Load Coupling for LTE Network Planning and Optimization”, IEEE Transactions on Wireless Communications, 11:6, June 2012. In this work, the inter-cell interference is explicitly taken into account in a cell-load coupling function, overcoming some of the drawbacks of said work of H. Kim et al. Drawbacks of this work include:                The cell load is not bounded to the maximum value of full load, the framework allows cells with a load of more than 100%        Does not provide an optimal cell partition, or any recommendation for setting the cell individual power offsets.        The optimization objective is limited to the minimization of the sum load of all cells.        
This framework was applied in Iana Siomina and Di Yuan: “Load Balancing in Heterogeneous LTE: Range Optimization via Cell Offset and Load-Coupling Characterization”, IEEE International Conference on Communications, pp. 1377-1381, Ottawa, Canada, Jun. 10-15, 2012 for load balancing in a heterogeneous network via a cell individual power offset given to the low power node (small cells). Drawbacks of this work include:                The load is balanced using Jain's fairness index as metric.        Only load balancing is considered (MLB only). There is no coordination or any other combination with physical base parameter optimization.        The solution is approached via a sequence of upper and lower bounds.        
A method and device for the optimization of base station antenna parameters in cellular wireless communication networks was described in EP1559289/U.S. Pat. No. 7,768,968. Drawbacks of this state of the art include:                only physical base station parameters are optimized, no load balancing parameter is used (CCO only)        the serving area of a base station is always determined by user locations having the highest received power of this base stations pilot or reference signal, there is no power offset for this received power used to increase the base station's serving area for the purpose of user association.        The target of load balancing is only seen as balancing the traffic demand distribution between the cells/base stations, not balancing the actual load of the base stations        The degree of load balancing cannot be chosen and is not automatically optimized in this method        the traffic demand per cell/base station is only taken into account by accumulating it over the base stations serving area defined above, the spatial distribution of the traffic demand is not taken into account in this method and device        Does not automatically suggest new sites in case existing sites are overloaded regardless of CCO        
A further general drawback of the state of the art for CCO and/or MLB is that it cannot be used to do cell outage compensation (COC).